Self-Adapting Language Models: Paper Authors Discuss Implications
Deep Papers
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Intro
This chapter explores the SEAL concept, emphasizing the necessity for language models to adapt their weights after deployment. The speakers discuss the limitations of static neural networks and introduce innovative self-editing mechanisms and techniques like synthetic data generation and reinforcement learning for enhancing model neuroplasticity.
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